Affect-Adaptive Activities in a Personalised Ubiquitous Learning System (original) (raw)
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Understanding student learning behaviors is at the prime importance of educational research. However, many complex factors influence learning processes, but one collective impact of all these factors is on human affect that influences learning and degree of motivation. In this study, we discuss the current state of human affect detection in education, our proposed affect change model and its implications. This study adopts dataset from ASSISTments online learning platform that consists of student interaction data, and ground truth labels for affect states coded by Baker Rodrigo Ocumpaugh Monitoring Protocol certified coders to develop and validate affect change model. We show that the proposed affect change model in combination with the adoption of machine learning algorithms will support the development of a ubiquitous learning system that tracks the student learning process in context of contributing factors and provide interventions when needed.
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Recent trends in Information and Communication Technology, Web based learning environment attract the learner for anywhere and anytime learning such as e-learning environment. Many research says that the learner's active listening duration is 15 to 20 minutes and research on e-learning mainly focusing to offer an adaptive e-learning content with respect to the learner's profile and knowledge. This paper, we are mainly focused, how to engage the student in e-learning for longer duration. To keep the learner, in active listening mood, we have to recognize the learner mood and offer the adaptive learning content with respect to their mood, knowledge in the domain, profile and Learner history feedback. We focused to reveal the learner's emotional behavior, we have taken Facial feature emotion extraction, body gesture, movement and EEG — Bio signal approach for emotion prediction. The result was analyzed and it shows that bio-signal accurately predicting the learner's emo...
2005
At the dawn of approaching information society, there is an ever-growing research interest on the field of elearning which drive attention of researchers from diverse area of disciplines, such as computer science, signal processing, psychology, education, etc. This is a reflection of a concrete consensus that Human Computer Interaction (HCI) is going to have a great impact on education in the future [1]. However, even though there are large numbers of studies on HCI, the affective aspects in HCI studies are still neglected [2]. It is argued that the current state of e-learning can be improved via inserting the adaptability and interactivity, which will be obtained by enhanced human-computer interaction, into student model [3,4]. This will facilitate the acceptance and credibility of the new generation of educational environments. In this paper, it is postulated that the possibility of integration of recent development in affective and cognitive computations will have a great impact in e-learning processes on education in the future.
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4th International Conference on Software and Data Technologies (ICSOFT 2009), 2009
This paper presents an educational system that incorporates two theories namely SAW and OCC in order to provide an improved affective e-learning environment. Simple additive Weighting (SAW) is used for the recognition of possible emotional states of the users, while the cognitive theory of emotions (OCC) is used for the generation of emotional states by educational agents. The system bases its inferences about users' emotions on user input evidence from the keyboard and the microphone, as two commonly used modalities of human-computer interaction. The actual combination of evidence from these two modes of interaction has been performed based on a sophisticated inference mechanism for emotions and a multi-attribute decision making theory. At the same time, user action evidence from the two modes of interaction also activates the cognitive mechanisms of the underlying OCC model that proposes emotional behavioural tactics for educational agents who act for pedagogic purposes. The presented educational system provides the important facility to authors to develop tutoring systems that incorporate emotional agents who can be parameterized so as to reflect their vision of teaching behaviour.
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proceedings of International …, 2010
In order to promote a more dynamic and flexible communication between the learner and the system, we present a structure of a new innovative and interactive e-learning system which implements emotion and level of cognition recognition. The system has as inputs the emotional and cognitive state of the user and re-organises the content and adjusts the flow of the course. Our concept aims to increase the learning efficiency of intelligent tutoring systems by using a combination of characteristics, such as content customization and user’s emotion recognition, and adapting all these features into a learner-centered educational system.
International Journal of Computer Applications, 2013
An Intelligent Tutoring System (ITS) should be able to select appropriate chunks of learning materials as well as evaluate learning outcomes while keeping in mind learner's various meta-cognitive and meta-affective factors. But literature review suggests that such systems are rare as they are complex and time consuming to develop. We have designed an adaptive intelligent tutoring system which is being implemented as a rules-based-expert-system for the dual purpose of-i) adaptive content selection and ii) evaluation of learning gain along with remedial actions. The system is in implementation stage and through this work, we inform in details about the developmental strategies adopted, e.g., use of Java Expert System Shell (JESS) for rules and fact base, Apachetomcat-server for Java implementation. This work also highlights the rule based implementation of domain and affective planner along with details about the rules in textual formats. Our student model is able to recognize learner's guessing (gaming) behavior, interest, independence, and confidence level. It can also differentiate-a learner's incorrect answer due to a guess from that due to lack of sufficient domain knowledge. This framework can be used as a guiding principle to build a more robust tutoring system by incorporating other student modeling attributes.
Toward Shaping the Learning Experience: An Experiment on Affective Mobile Learning
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In this paper we attempt to bridge the fields of mobile learning and user-adaptive systems research by means of affective computing. In a field experiment we investigated how different environments and ways to present content on mobile devices influence users' emotions during and their recognition after learning information. Our aim was to find out how learning content should be presented and adapted so that equal learning success across different situations can be achieved by learners. The results show that learners' emotional stress, as indexed by heart rate and skin conductivity, was significantly higher in a noisy and crowded shopping mall than in a quiet café. Learners' emotions strongly affected their learning abilities as reflected by measurements of recognition, which was significantly higher for information learned in the café than in the shopping mall. The results show also that participants recalled textual content better if it was presented in chunks via one or more (web-) pages as opposed to a continuous text that required manual scrolling. Based on the results we present initial recommendations for how affective mobile learning services may adapt the presentation of learning content in order to counterbalance environmental effects on learning and thus to assist a learner. While this study confirms earlier results in environmental psychology and emotion research we conclude that it provides an initial empirical base for the design and development of innovative mobile learning applications and services that adapt learning content on the basis of learners' emotions. This method could counterbalance impeding environmental, cognitive, and emotional influences on learners, shape their learning experiences and thus assist and support mobile learning.
Towards an Interactive E-Learning System Based on Emotions and Affective Cognition
In order to promote a more dynamic and flexible communication between the learner and the system, we present a structure of a new innovative and interactive e-learning system which implements emotion and level of cognition recognition. The system has as inputs the emotional and cognitive state of the user and re-organises the content and adjusts the flow of the course. Our concept aims to increase the learning efficiency of intelligent tutoring systems by using a combination of characteristics, such as content customization and user’s emotion recognition, and adapting all these features into a learner-centered educational system.
Adapting Learning Activities Selection in an Intelligent Tutoring System to Affect
2018
My PhD focuses on adapting learning activities selection to learner affect in an intelligent tutoring system. The research aims to investigate the a.,han via sensors or questionnaires. The research will use of a mixture of qualitative and quantitative methods to achieve these aims. This research will significantly contribute to the area of intelligent tutoring technology by providing more insights into how to adapt to affective states, and improve the delivery of learning. The result will lead to an algorithm for learning activity selection based on affect, which also incorporates other relevant learner characteristics, such as personalty, that moderate affect.